The problem of air environment has become the focus of attention. Apart from the exhaust emissions from factories, the popularity of private cars has led to worrisome air conditions. Related government agencis have also begun to strengthen the control of air environment, and put forward relevant policies for grid monitoring of environmental quality. In this context, many micro-monitoring instruments have emerged into the market, but due to the inadequate accuracy of internal sensors, there is a problem of data deviation. In order to solve this problem, this study uses the Long Short-Term Memory (LSTM) model of neural network technology and semi-supervised learning method to improve the accuracy of monitoring data. By comparing with other models, this method achieves a sound effect.